People Directory

Nadya Shusharina, PhD

Instructor, Harvard Medical School
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Suite 320, 125 Nashua St.
Boston MA, 02114
(617) 943 6968

Dr. Shusharina graduated with a PhD in Physics and Mathematics from Moscow State University, Russia. She completed post-doctoral training in polymer physics at Lund University, SUNY at Buffalo, and UNC at Chapel Hill. In 2008 she completed the Career Reengineering Program at the MIT which combined coursework and internship in bioengineering that allowed her to enter the field of medical physics.

Board certification

Medical Dosimetrist, The Medical Dosimetry Certification Board

Research Interests 

Dr. Shusharina's research is centered on medical image analysis and its applications for radiotherapy. She has developed medical image registration algorithms, and contributed to Plastimatch and 3D Slicer open source software projects. Dr. Shusharina participated in National Alliance for Medical Image Computing (NA-MIC) 3D Slicer developers meetings in 2011-2015, presenting research and tutorials. Her current work focuses on the probabilistic approaches to the clinical target volume (CTV) definition in radiotherapy, and development of automated algorithms for CTV delineation and analysis.  

MICCAI Challenge

MICCAI 2020 Challenge, a contest for brain image segmentation she is currently running, is an important contribution to the medical imaging community.   

TCIA Collection

Glioma Image Segmentation for Radiotherapy: RT targets, barriers to cancer spread, and organs at risk (GLIS-RT)

Recent Publications (Google Scholar)

Definition of the Clinical Target Volume

Bortfeld, N. Shusharina, D. Craft. Probabilistic definition of the clinical target volume—implications for tumor control probability modeling and optimization. Phys Med Biol 2021; 66:01NT01.

Unkelbach, T. Bortfeld, C. E. Cardenas, V. Gregoire, W. Hager, B. Heijmen, R. Jeraj, S. S. Korreman, R. Ludwig, B. Pouymayou, N. Shusharina, J. Söderberg, I. Toma-Dasu, E. G.C. Troost, E. Vasquez Osorio. The role of computational methods for automating and improving clinical target volume definition. Radiother Oncol 2020; 153:15-25,

N. Shusharina, D. Craft, Y.-L. Chen, H. Shih, T. Bortfeld. The clinical target distribution: a probabilistic approach to the clinical target volume. Phys Med Biol 2018; 63:155001.

N. Shusharina, J. Söderberg, D. Edmunds, F. Löfman, H. Shih, T. Bortfeld. Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume. Radiother Oncol 2020; 146:37-43. Doi:

Medical Image Analysis

N. Shusharina, G. C. Sharp. Analytic regularization for landmark-based image registration. Phys Med Biol 2012; 57:1477-1498.

N. Shusharina, G. C. Sharp. Image registration using radial basis functions with adaptive radius. Med Phys 2012; 39:6542-6549.

J. Shackleford, Q. Yang, A. Lourenço, N. Shusharina, N. Kandasamy, G. Sharp. Analytic regularization of uniform cubic B-spline deformation fields. LNCS 2012; 7511:122-129.

Z. H. Saleh, A. P. Apte, G. C. Sharp, N. P. Shusharina, Y. Wang, H. Veeraraghavan, M. Thor, L. P. Muren, S. S. Rao, N. Y. Lee, and J. O. Deasy. The distance discordance metric - A novel approach of quantifying spatial uncertainties in intra- and inter-patient deformable image registration. Phys Med Biol 2014; 59:733-746.

G. Sharp, K. D. Fritscher, V. Pekar, M. Peroni,  N. Shusharina, H. Veeraraghavan, J. Yang. Perspectives on Automated Image Segmentation for Radiotherapy. Med Phys 2014, 41:050902.

J. A. Shackleford, N. Shusharina, J Verburg, G. Warmerdam, B. Winey, M. Neuner, P. Steininger, A. Arbisser, P. Golland, Y. Lou, C. Paganelli, M. Peroni, M. Riboldi, G. Baroni, P. Zaffino, M. F. Spadea, A. Apte, Z. Saleh, J. O. Deasy, S. Mori, N. Kandasamy, G. C. Sharp.  Plastimatch 1.6 -- current capabilities and future directions. MICCAI 2012, Proceedings of the First International Workshop on Image-Guidance and Multimodal Dose Planning in Radiation Therapy. 

Clinical Research

N. Shusharina, J. Cho, G. Sharp, N. C. Choi. Correlation of 18F-FDG avid volumes on pre- and post-radiotherapy PET scans in recurrent lung cancer. Int J Rad Oncol Biol Phys 2014; 89:137-144.

N. Shusharina, G. C. Sharp, N. C. Choi. In reply to Saraiya et al. Int J Rad Oncol Biol Phys 2014, 90:969-970.

N. Shusharina, B. Fullerton, J. Adams, G. Sharp, A. Chan. Impact of aeration change and beam arrangement on the robustness of proton plans. J Appl Clin Med Phys 2019; 20:14-21.

N. Shusharina, Z. Liao, R. Mohan, A. Liu, A. Niemierko, N. Choi, T. Bortfeld. Differences in lung injury after IMRT or proton therapy assessed by 18FDG PET imaging. Radiother Oncol 2018; 128:147-153.

A. L. McNamara, D. C. Hall, N. Shusharina, A. Liu, X. Wei, A. Ajdari, R. Mohan, Z. Liao, H. Paganetti Perspectives on the model-based approach to proton therapy trials: a retrospective study of a lung cancer randomized trial. Radiotherapy and Oncology. 2020;147:8-14. doi:10.1016/j.radonc.2020.02.022.




Located in: Research